Parametric tuning of the Gielis Superformula for non-target based automated evolution of 3D Printable objects

3D printing is an emerging trend fuelled by the rapid technology advancements in 3D printing technology. Printing out 3D designs is something new and interesting but the process of designing 3D objects is far from effortless. Researchers have recently forged ahead in conducting numerous studies on using mathematical formulas to create objects and shapes in 3D space. A mathematical encoding for geometric shapes called the Superformula was proposed by Johan Geilis through the generalization of the Supereclipse formula to generate 3D shapes and objects by extending its spherical products. The focus of this study is to investigate the ideal range of parametric values supplied to the Superformula in order to automatically generate 3D shapes and objects through the use of Evolution Algorithms (EAs). Thus, Evolutionary Programming was used as the EA in this study which serves as the main evolution component that uses a fitness function tailored in a way that it is able to evaluate the 3D objects and shapes generated by the Superformula. The values require by the Superformula to generate 3D objects or shapes are . To obtain the ideal range of values for the afore mentioned parameters, five different sets of experiments were carried out within the range set of {0 - 20}, {0 - 40}, {0 - 60}, {0 - 120}, and {0 - 240}.Each range set of numbers will be tested five times and the final objects from each of the runs were then analysed. From the observations obtained, the range set of {0- 20}, {0- 60}, and {0- 120} shows the most promising results as the final objects produced were unique and it was surmised that within these range of numbers contain highly unique and novel 3D objects and shapes

A Time-Critical Investigation of Parameter Tuning in Differential Evolution for Non-Linear Global Optimization

Parameter searching is one of the most important aspects in getting favorable results in optimization problems. It is even more important if the optimization problems are limited by time constraints. In a limited time constraint problems, it is crucial for any algorithms to get the best results or near-optimum results. In a previous study, Differential Evolution (DE) has been found as one of the best performing algorithms under time constraints. As this has help in answering which algorithm that yields results that are near-optimum under a limited time constraint. Hence to further enhance the performance of DE under time constraint evaluation, a throughout parameter searching for population size, mutation constant and f constant have been carried out. CEC 2015 Global Optimization Competition’s 15 scalable test problems are used as test suite for this study. In the previous study the same test suits has been used and the results from DE will be use as the benchmark for this study since it shows the best results among the previous tested algorithms. Eight different populations size are used and they are 10, 30, 50, 100, 150, 200, 300, and 500. Each of these populations size will run with mutation constant of 0.1 until 0.9 and from 0.1 until 0.9. It was found that population size 100, Cr = 0.9, F=0.5 outperform the benchmark results. It is also observed from the results that good higher Cr around 0.8 and 0.9 with low F around 0.3 to 0.4 yields good results for DE under time constraints evaluatio

Exploration of mutation step sizes in the automated evolution of printable free-form 3D objects

3D printing is a comparatively new technology that is becoming ever more attractive to everyday practitioners and hobbyists due to its low start-up cost as well as making significant advancements in its printing process as well resolution and material variety. Implementation of EAs in the field of 3D printing is still in its infancy since 3D printing itself is a relatively new technology that has only become main stream due to its significant decrease in acquisition cost in the past 2-3 years. In this study, an EA in the form of Evolutionary Programming (EP) is used to automatically evolve 3D objects generated by Gielis’ Superformula. Objective: The focus of this study is to explore the mutation step size in hoping to create more diverse populations in the evolution of the generated 3D printable objects. In EP, the operator responsible for offspring generation is through the mutation process solely. Hence, the mutation step size has a direct and very significant impact on the diversity of the offspring generated. A fitness function was designed to evaluate the 3D objects and shapes generated by the Superformula. The parameters for the Superformula to generate 3D objects or shapes are m_1, m_2, n_(1,1), n_(1,2), n_(1,3), n_(2,1), n_(2,2), and n_(2,3). These parameters serve as a representation in EP and the mutation step size will affect the chances of these parameters’ values to change. To carry out this study, ten different mutation step sizes ranging from 0.1 to 1.0 in increments of 0.1 were used and run for five times. Results: The results indicate that the most aesthetically-pleasing as well as machine-printable results were obtained using the smallest mutation size of 0.1. Conclusion: Optimal setting for mutation rate can successful generate 3D-printable shapes that are aesthetically-pleasing using the proposed Gielis Superformula-based methodology

Automated synthesis of mobile game environments and rulesets using a hybridized interactive evolutionary programming approach

By hybridizing Evolutionary Programming (EP) with Interactive Evolutionary Algorithm (IEA), game rules and its playing environment will be automatically generated for an arcade-type game that can be played on the Android mobile platform. In this study, mutation rates of 0.7 and 0.9 are used to generate both the game rules and the game environment for the mobile game. Players are used as the evaluator instead of the conventional mathematical fitness functions and hence the motivation for using high mutation rate is that they are able to generate higher levels of diversity during the optimization runs. This interactive mode of game-playing cum evaluation will enable the creation of games that can fit the user’s preferences as well as styles of game-playing. Experiments show a very positive result where very good evaluation scores were obtained from the users. This shows that with a high mutation rate, the hybridized EP with IEA approach can generate rules and environments that are well-accepted and liked by human players

A time-critical investigation of parameter tuning in differential evolution for non-linear global optimization

Parameter searching is one of the most important aspects in getting favorable results in optimization problems. It is even more important if the optimization problems are limited by time constraints. In a limited time constraint problems, it is crucial for any algorithms to get the best results or near-optimum results. In a previous study, Differential Evolution (DE) has been found as one of the best performing algorithms under time constraints. As this has help in answering which algorithm that yields results that are near-optimum under a limited time constraint. Hence to further enhance the performance of DE under time constraint evaluation, a throughout parameter searching for population size, mutation constant and f constant have been carried out. CEC 2015 Global Optimization Competition’s 15 scalable test problems are used as test suite for this study. In the previous study the same test suits has been used and the results from DE will be use as the benchmark for this study since it shows the best results among the previous tested algorithms. Eight different populations size are used and they are 10, 30, 50, 100, 150, 200, 300, and 500. Each of these populations size will run with mutation constant of 0.1 until 0.9 and from 0.1 until 0.9. It was found that population size 100, Cr = 0.9, F=0.5 outperform the benchmark results. It is also observed from the results that good higher Cr around 0.8 and 0.9 with low F around 0.3 to 0.4 yields good results for DE under time constraints evaluatio

A comprehensive comparison of evolutionary optimization limited by number of evaluations against time constraints

In this study, the importance of optimization problems constrained by time is highlighted. Practically allevolutionary optimization studies have focused exclusively on the use of number of fitness evaluations as the constraining factor when comparing different evolutionary algorithms (EAs). This investigation represents the first study which empirically compares EAs based on time-based constraints against number of fitness evaluations. EAs which yield an optimum or near-optimum solutions is crucial for real-time optimization problems. Which EAs are able to provide near optimum solutions in time limited real-time optimization problems has never been answered before. To find out the answer for this question, four well-known and most commonly-used algorithms are tested. Particle swarm optimization (PSO), Differential Evolution (DE), Genetic Algorithms (GA), and Covariance Matrix Adaptation Evolution Strategy (CMA-ES) are tested in three different setups of experiments. A comprehensive and latest global optimization benchmark test suite is used in the form of the CEC 2015 Global Optimization Competition’s 15 scalable test problems. The first experiment is to test the performance of these algorithms in expensive benchmark optimization problems that limit the number of fitness evaluations to 50N where N represents the number of optimization dimensions. The second experiment allows these algorithms to run up to the full 10000N evaluations. The last experiment will compare the performance of these algorithms limited by time to 300 milliseconds. The results obtained shows that DE can perform well in the 50N and 10000N evaluation. Critically, we have shown for the first time that in time-limited situations, DE is also the frontrunner by obtaining clearly better results compared to the other three well-known and widely used EAs

How do first impressions affect perceived approachability?

This essay explores the impact of first impressions on perceived approachability in social interactions, considering factors like facial expressions, attire, and vocal cues. It establishes approachability as the dependent variable influenced by first impressions, measured by voice cues, facial expressions, attire, and non-verbal cues while controlling for age, gender, race, height, and personality traits. In the literature review, we examine two key studies, focusing on face-based and voice-based impressions in a Chinese sample and rapid threat judgments based on facial appearance. The essay underscores the importance of non-verbal cues on first impressions and approachability. To understand the relationship, we use a survey questionnaire to gather insights into how the first impression affects perceptions. Data collection involves Qualtrics surveys and non-probability sampling, with analysis using ANOVA and Regression analysis. The essay contributes valuable insights into the complexities of how initial judgments influence perceived approachability, aiming to enhance comprehension and guide future research in this field

A Comparative Study on three Component Selection Mechanisms for Hyper-Heuristics in Expensive Optimization

Numerous studies in optimization problems often lead to tailoring a specific algorithm to adapt to the problem instances, especially in expensive optimization problems. The focus of these researches is often to challenge and outperform another algorithm in the specific problem instant. Once the problem instants changes, more tailoring of the algorithm has to be done in order for the algorithm to perform at an optimum level. Expensive optimization often requires a large amount of resources to run on such as computational power, high run-time budget and consumes a lot of time. As such, tailoring an algorithm to perform well in expensive optimization requires a lot of expertise and time. Hyper-heuristics is an approach that utilizes a set of Low-Level Heuristic (LLH) and a selection mechanism to solve expensive optimization problems. The main aim of using hyper-heuristics is to be able to apply a general yet efficient optimizationalgorithm to all expensive problem instances with very minor or minimal tweaks. In this study, three different selection mechanisms for Hyper-heuristics are introduced and compared against one of the top performing expensive optimization algorithms known asthe Mean-Variance Mapping Optimization (MVMO) as described in the CEC 2015 and 2016 expensive optimization competitions. Three variants of hyper-heuristics were used in this study, Simple Random All Moves Acceptance (SRAMA), Tabu-Search All Moves Acceptance (TSAMA) and Random Gradient Descent All Moves Acceptance (RGDAMA). The set of LLH will also include a simplified version of MVMO. The performance of hyper-heuristics is highly encouraging against a specifically tailored algorithm for CEC test set of expensive optimization problems

A Systematic Exploration of Mutation Space in a Hybridized Interactive Evolutionary Programming for Mobile Game Programming

In this study, a systematic exploration of mutation space in interactive evolutionary programming was conducted to investigate the effects of the game synthesis process using different mutation rates. Evolutionary programming is the core Evolutionary Algorithm (EA) used in this study where it is hybridized with Interactive Evolutionary Algorithm (IEA) to generate different rulesets that was played on a custom arcade-type mobile game. The experiment was initially conducted by utilizing different mutation rates of 10, 20, 30, 40, 50, 60, 70, 80, and 90 percent. From the optimization results obtained, the single best individual was selected from each mutation rate to further analyze its quality. It was discovered that higher mutation rates were able to yield faster and better solutions and lower mutation rates generally yielded results that were below average

Empirical Evaluation of Mutation Step Size in Automated Evolution of Non-Target-Based 3D Printable Objects

Evolutionary algorithms (EA) currently play a central role in solving complex, highly non-linear problems such as in engineering design, computational optimization, bioinformatics and many more diverse fields. Implementation of EAs in the field of 3D printing is still in its infancy since 3D printing itself is a relatively new technology that has only become main stream due to its significant decrease in acquisition cost in the past 2-3 years. Due to the rapid uptake by everyday hobbyists and the significant advancements being made in material diversity, 3D printing will only continue its rapid expansion into our everyday lives. In this study, an EA in the form of Evolutionary Programming (EP) is used to automatically evolve 3D objects generated by Geilis’s Superformula. The focus of this study is to explore the mutation step size in hoping to create more diverse populations in the evolution of the generated 3D printable objects. In EP, the operator responsible for offspring generation is through the mutation process solely. Hence, the mutation step size has a direct and very significant impact on the diversity of the offspring generated. A fitness function was design to evaluate the 3D objects and shapes generated by the Superformula. The parameters for the Superformula to generate 3D objects or shapes are These parameters serve as a representation in EP and the mutation step size will affect the chances of these parameters’ values to change. To carry out this study, five different mutation step sizes were used and each mutation step size will be run for five times. The mutation step sizes are 0.1, 0.2, 0.4, 0.6 And 0.8. From the results obtained, a mutation step size 0.1 shows a more stable population pool and were able to generate diverse and distinctive 3D objects